Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8342957
Yongjie Zhang, Yanyun Xu, Zheng Yang, Qin Sun
In order to obtain more accurate Lemaitre high cycle fatigue damage parameters, a BFGS quasi-Newton fitting method is proposed by optimizing the residual difference between the predicted life and actual life in the least square method. The damage parameters are solved in the damage evolution equation of metal high cycle fatigue. Taking 2A12-T4 aluminum alloy as an example, the precision and solution efficiency of BFGS quasi-Newton fitting method were verified for the evaluation of metal high cycle fatigue life.
{"title":"Study on BFGS fitting method of metal high cycle fatigue damage parameters","authors":"Yongjie Zhang, Yanyun Xu, Zheng Yang, Qin Sun","doi":"10.1109/ICSESS.2017.8342957","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342957","url":null,"abstract":"In order to obtain more accurate Lemaitre high cycle fatigue damage parameters, a BFGS quasi-Newton fitting method is proposed by optimizing the residual difference between the predicted life and actual life in the least square method. The damage parameters are solved in the damage evolution equation of metal high cycle fatigue. Taking 2A12-T4 aluminum alloy as an example, the precision and solution efficiency of BFGS quasi-Newton fitting method were verified for the evaluation of metal high cycle fatigue life.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127719655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343013
Binhan Xu, Shuyu Chen, Hancui Zhang, Tianshu Wu
The intrusion or attack in the computer network is one of the most important issues in Cloud environment. Due to enormous network traffic, dynamic and incremental learning is important to intrusion detection system (IDS) in Cloud. In existing incremental algorithms, k Nearest Neighbors (k-NN) has the advantage of dealing with the huge and incremental multi-class nature of data. However, k-NN algorithm has poor performance in classification. Support Vector Machine (SVM) is an extraordinary classification method widely used in intrusion detection field, while its training time increases sharply with expansion of training data. Therefore, we proposed Incremental k-NN SVM method using combination of k-NN and SVM, bringing advantages of the both methods. In this approach an R∗-tree provides efficient expansion of training data and query for k-NN. Experiments on open dataset KDDCUP 99 indicates that Incremental k-NN SVM intrusion detection method has the ability to learn and update with new data in acceptable time, and its predicting time does not increase rapidly along the incremental learning process.
{"title":"Incremental k-NN SVM method in intrusion detection","authors":"Binhan Xu, Shuyu Chen, Hancui Zhang, Tianshu Wu","doi":"10.1109/ICSESS.2017.8343013","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343013","url":null,"abstract":"The intrusion or attack in the computer network is one of the most important issues in Cloud environment. Due to enormous network traffic, dynamic and incremental learning is important to intrusion detection system (IDS) in Cloud. In existing incremental algorithms, k Nearest Neighbors (k-NN) has the advantage of dealing with the huge and incremental multi-class nature of data. However, k-NN algorithm has poor performance in classification. Support Vector Machine (SVM) is an extraordinary classification method widely used in intrusion detection field, while its training time increases sharply with expansion of training data. Therefore, we proposed Incremental k-NN SVM method using combination of k-NN and SVM, bringing advantages of the both methods. In this approach an R∗-tree provides efficient expansion of training data and query for k-NN. Experiments on open dataset KDDCUP 99 indicates that Incremental k-NN SVM intrusion detection method has the ability to learn and update with new data in acceptable time, and its predicting time does not increase rapidly along the incremental learning process.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133764960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343032
B. Shan, D. Jia, Lu Zhang, Fang Cao, Yi Sun
The electric power alteration strategy can realize the substitution of the power supply for the coal and the fuel in the terminal energy consumption, which can realize the fundamental change of energy development mode. In this paper, to provide theoretical guidance for grid planning, energy demand forecasting model influencing factors in the context of electric power alteration are thoroughly analyzed, which concludes four key influence factors including environmental protection pressure restriction, energy price fluctuation, policy support and technical substitution. Combined all the factors, energy demand forecasting models in the context of electric power alteration including the Markal model(Market Allocation Model), LEAP model, MAED model(Model for Analysis of Energy Demand) are built, which can provide references for electric power alteration in China.
{"title":"Analysis of energy demand forecasting model in the context of electric power alteration","authors":"B. Shan, D. Jia, Lu Zhang, Fang Cao, Yi Sun","doi":"10.1109/ICSESS.2017.8343032","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343032","url":null,"abstract":"The electric power alteration strategy can realize the substitution of the power supply for the coal and the fuel in the terminal energy consumption, which can realize the fundamental change of energy development mode. In this paper, to provide theoretical guidance for grid planning, energy demand forecasting model influencing factors in the context of electric power alteration are thoroughly analyzed, which concludes four key influence factors including environmental protection pressure restriction, energy price fluctuation, policy support and technical substitution. Combined all the factors, energy demand forecasting models in the context of electric power alteration including the Markal model(Market Allocation Model), LEAP model, MAED model(Model for Analysis of Energy Demand) are built, which can provide references for electric power alteration in China.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134021542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8342875
R. R. Rani, D. Ramyachitra
The Cancer Feature Selection and classification problem is one of the prevalent tasks in computational molecular biology. Detecting a gene or list of genes which cause cancer can be acknowledged using the feature selection and classification which leads to giving a faultless treatment for patient and drug discovery of the particular gene. The feature selection and classification of cancer using microarray gene expression data is a computationally difficult task. Even now, the computation of gene selection and classification is a challenging area to provide an exact biological related gene that causes cancer. In this work, three methods have been proposed. One is the Fish Swarm Optimization algorithm along with both Support Vector Machine and Random Forest technique for cancer feature selection and classification. But the above methods have reduced very few features from the datasets. Thus, they are considered as an existing method for this work. Now, the second proposed method namely an enhanced Krill Herd Optimization (KHO) technique was employed for selecting the genes and Random Forest (RF) Technique was employed to classify the cancer types. The Random Forest classification has been used because of its accurate classification accuracy. First, the subset of features is selected using KHO and the Random Forest classification is applied to the selected features. Ten different gene microarray cancer datasets were used to evaluate the efficiency of the proposed. The proposed KHO/RF method is compared with other well-known existing methods like PSO/SVM, PSO/RF, FSO/SVM and FSO/RF. As an outcome, the proposed method outperforms the other existing methods with 100% accuracy of results for most datasets.
{"title":"Krill Herd Optimization algorithm for cancer feature selection and random forest technique for classification","authors":"R. R. Rani, D. Ramyachitra","doi":"10.1109/ICSESS.2017.8342875","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342875","url":null,"abstract":"The Cancer Feature Selection and classification problem is one of the prevalent tasks in computational molecular biology. Detecting a gene or list of genes which cause cancer can be acknowledged using the feature selection and classification which leads to giving a faultless treatment for patient and drug discovery of the particular gene. The feature selection and classification of cancer using microarray gene expression data is a computationally difficult task. Even now, the computation of gene selection and classification is a challenging area to provide an exact biological related gene that causes cancer. In this work, three methods have been proposed. One is the Fish Swarm Optimization algorithm along with both Support Vector Machine and Random Forest technique for cancer feature selection and classification. But the above methods have reduced very few features from the datasets. Thus, they are considered as an existing method for this work. Now, the second proposed method namely an enhanced Krill Herd Optimization (KHO) technique was employed for selecting the genes and Random Forest (RF) Technique was employed to classify the cancer types. The Random Forest classification has been used because of its accurate classification accuracy. First, the subset of features is selected using KHO and the Random Forest classification is applied to the selected features. Ten different gene microarray cancer datasets were used to evaluate the efficiency of the proposed. The proposed KHO/RF method is compared with other well-known existing methods like PSO/SVM, PSO/RF, FSO/SVM and FSO/RF. As an outcome, the proposed method outperforms the other existing methods with 100% accuracy of results for most datasets.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130358402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343005
Bo Wang, Zhiqiang Wei, Z. Li, Wei Hu, Shuang Wang, Shugang Zhang, Changehe Du, Wenjuan Shi
Coastal environment is one of the most important part in human activities environment, so coastline studying has great significance to us. Since the coastline stretches widely, shot by many cameras at the same time in different regions currently, and we need spliced it into a complete coastline for further study. However, the feature points of the beach are difficult to be extracted, we use the region-based template matching method to splice it in this paper. An efficient template matching method was proposed to solve the time-consuming classical algorithm of exhaustive search and low efficiency of template selection. Through the experiments, most of the coastline can be spliced better and faster than MAD method.
{"title":"Region-based template matching method for multi-view coastline image stitching","authors":"Bo Wang, Zhiqiang Wei, Z. Li, Wei Hu, Shuang Wang, Shugang Zhang, Changehe Du, Wenjuan Shi","doi":"10.1109/ICSESS.2017.8343005","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343005","url":null,"abstract":"Coastal environment is one of the most important part in human activities environment, so coastline studying has great significance to us. Since the coastline stretches widely, shot by many cameras at the same time in different regions currently, and we need spliced it into a complete coastline for further study. However, the feature points of the beach are difficult to be extracted, we use the region-based template matching method to splice it in this paper. An efficient template matching method was proposed to solve the time-consuming classical algorithm of exhaustive search and low efficiency of template selection. Through the experiments, most of the coastline can be spliced better and faster than MAD method.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8342861
Chunjiang Yan, Chuang Wang, J. Du, Hualin Fang, Yixuan Wang, Xuezhi Xiang, Xinli Guo
A two-step method based on deep learning is proposed for the intrusion detection of engineering vehicles working under high power transmission lines. In the first step, intrusion detection algorithm is used to identify the potential target area. Then the results are supplied to a trained deep convolution neural network classifier. This way combining intrusion detection method with CNN, the invasion of the engineering vehicles under high power transmission lines can efficiently be detected up to an accuracy of 97.2 %.
{"title":"Intrusion detection for engineering vehicles under the transmission line based on deep learning","authors":"Chunjiang Yan, Chuang Wang, J. Du, Hualin Fang, Yixuan Wang, Xuezhi Xiang, Xinli Guo","doi":"10.1109/ICSESS.2017.8342861","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342861","url":null,"abstract":"A two-step method based on deep learning is proposed for the intrusion detection of engineering vehicles working under high power transmission lines. In the first step, intrusion detection algorithm is used to identify the potential target area. Then the results are supplied to a trained deep convolution neural network classifier. This way combining intrusion detection method with CNN, the invasion of the engineering vehicles under high power transmission lines can efficiently be detected up to an accuracy of 97.2 %.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115469372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343055
Md. Hasan Mahmood, M. S. Hosain
Test case prioritization involves prioritized the test cases for regression testing which improve the effectiveness of the testing process. By improving test case scheduling we can optimize time and cost as well as can produce better tested products. There are a number of methods to do prioritized test cases but not that effective or practical for the real-life large commercial systems. Most of the technique deals with finding defects or covering more test cases. In this paper, we will extend the previous work to incorporate real life practical aspects to schedule test cases. This will cover most of the businesses functionally based on the practical aspects. This approach covers more business area and ensure more defects. By prioritized test cases with this technique we will cover most important business functionally with less number of test cases.
{"title":"Improving test case prioritization based on practical priority factors","authors":"Md. Hasan Mahmood, M. S. Hosain","doi":"10.1109/ICSESS.2017.8343055","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343055","url":null,"abstract":"Test case prioritization involves prioritized the test cases for regression testing which improve the effectiveness of the testing process. By improving test case scheduling we can optimize time and cost as well as can produce better tested products. There are a number of methods to do prioritized test cases but not that effective or practical for the real-life large commercial systems. Most of the technique deals with finding defects or covering more test cases. In this paper, we will extend the previous work to incorporate real life practical aspects to schedule test cases. This will cover most of the businesses functionally based on the practical aspects. This approach covers more business area and ensure more defects. By prioritized test cases with this technique we will cover most important business functionally with less number of test cases.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"323 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122710420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8342994
Xiang Wang, Bin Xu, Weike Wang, Lin Li, Pei Du, Cheng Zhou, Mingzhe Li, Tongsheng Xia
Most embedded systems contain a number of software vulnerabilities, such as program buffer overflow. The physical attacks in embedded systems are also becoming more and more common. This paper presents a fast, effective and reliable algorithm for tagging and validating what can be used in embedded systems. The compiler automatically collects the secure tags for each main memory segment at compile time. At run-time, the designed hardware observes the dynamic execution trace, and checks whether the trace conforms to the permissible behavior and triggers the appropriate response mechanisms according to the check result. This design does not change the compiler or the existing instruction set, with no restriction on the software developer. The design is implemented on an actual SOPC platform. Experimental analysis shows that the proposed techniques can eliminate a wide range of common software and physical attacks, with low performance penalties and minimal overheads.
{"title":"A novel security validation in embedded system","authors":"Xiang Wang, Bin Xu, Weike Wang, Lin Li, Pei Du, Cheng Zhou, Mingzhe Li, Tongsheng Xia","doi":"10.1109/ICSESS.2017.8342994","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342994","url":null,"abstract":"Most embedded systems contain a number of software vulnerabilities, such as program buffer overflow. The physical attacks in embedded systems are also becoming more and more common. This paper presents a fast, effective and reliable algorithm for tagging and validating what can be used in embedded systems. The compiler automatically collects the secure tags for each main memory segment at compile time. At run-time, the designed hardware observes the dynamic execution trace, and checks whether the trace conforms to the permissible behavior and triggers the appropriate response mechanisms according to the check result. This design does not change the compiler or the existing instruction set, with no restriction on the software developer. The design is implemented on an actual SOPC platform. Experimental analysis shows that the proposed techniques can eliminate a wide range of common software and physical attacks, with low performance penalties and minimal overheads.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123886948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343018
Tao Li, Wei Li, Lei Yang
The spacecraft electrical power system (EPS) employs photovoltaic array (PVA) to supply power to platform and user loads. Thus, it is important to predict power production capability of PVA for spacecraft mission planning and certification of on-orbit flight operating. The PVA performance is primarily determined by irradiation level, temperature, and solar array shadowing. To provide an accurate and flexible method for analyzing spacecraft PVA performance, the author developed spacecraft Solar Array Electrical Performance Simulation system (SAEPS). The SAEPS was consisted of 5 modules: simulation setting module, orbit mechanics module, geometric model control module, shadow analysis module, power supply capability analysis module. The current vs. voltage (I-V) model of photovoltaic (PV) cell under on-orbit irradiation intensity, temperature and shadow was built. The model of PV string, which is composed of PV cells, bypass diodes and block diodes, is built. Output current of PVA is obtained by summing all strings output currents at predefined bus voltage. Simulation was performed for a typical spacecraft in this paper. Results show this method is applicable to arbitrary illumination levels, temperatures and shadow patterns, and accuracy improved 20% compared to traditional method.
{"title":"Development of spacecraft solar array electrical performance simulation system","authors":"Tao Li, Wei Li, Lei Yang","doi":"10.1109/ICSESS.2017.8343018","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343018","url":null,"abstract":"The spacecraft electrical power system (EPS) employs photovoltaic array (PVA) to supply power to platform and user loads. Thus, it is important to predict power production capability of PVA for spacecraft mission planning and certification of on-orbit flight operating. The PVA performance is primarily determined by irradiation level, temperature, and solar array shadowing. To provide an accurate and flexible method for analyzing spacecraft PVA performance, the author developed spacecraft Solar Array Electrical Performance Simulation system (SAEPS). The SAEPS was consisted of 5 modules: simulation setting module, orbit mechanics module, geometric model control module, shadow analysis module, power supply capability analysis module. The current vs. voltage (I-V) model of photovoltaic (PV) cell under on-orbit irradiation intensity, temperature and shadow was built. The model of PV string, which is composed of PV cells, bypass diodes and block diodes, is built. Output current of PVA is obtained by summing all strings output currents at predefined bus voltage. Simulation was performed for a typical spacecraft in this paper. Results show this method is applicable to arbitrary illumination levels, temperatures and shadow patterns, and accuracy improved 20% compared to traditional method.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124224055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8342922
Tianyi Lan, Qing Han, Hongwei Fan, Julong Lan
While there has been a belief over the past few years that virtual network functions (VNFs) should be built on common servers, we argue that it can lead to limited performance and large up/down traffic. This paper proposes a new idea of shifting part of NFV functions from software packages to common hardware devices to promote overall performance. Then we present the design and implementation of PPAP, a Packets Processing Acceleration Platform for NFV. It offers high flexibility by allowing functions to control the processing flow of hardware. Dynamic match tables and virtualization techniques ensure isolation among VNF instances.
{"title":"FPGA-based packets processing acceleration platform for VNF","authors":"Tianyi Lan, Qing Han, Hongwei Fan, Julong Lan","doi":"10.1109/ICSESS.2017.8342922","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342922","url":null,"abstract":"While there has been a belief over the past few years that virtual network functions (VNFs) should be built on common servers, we argue that it can lead to limited performance and large up/down traffic. This paper proposes a new idea of shifting part of NFV functions from software packages to common hardware devices to promote overall performance. Then we present the design and implementation of PPAP, a Packets Processing Acceleration Platform for NFV. It offers high flexibility by allowing functions to control the processing flow of hardware. Dynamic match tables and virtualization techniques ensure isolation among VNF instances.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130027180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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